Towards personalized management of drug interactions: from interaction to
drug-drug-gene-interaction
Bahar, Akbar
DOI:10.33612/diss.112160601
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Bahar, A. (2020). Towards personalized management of drug interactions: from drug-drug-interaction to drug-drug-gene-interaction. University of Groningen. https://doi.org/10.33612/diss.112160601
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Impact of Drug-Gene-Interaction,
Drug-Drug-Interaction, and Drug-Drug-Gene-Interaction on
Switching, Dose Adjustment and Early Discontinuation
of (Es)citalopram: an Explorative Cohort Study
within the PharmLines Initiative
Muh. Akbar Bahar Pauline Lanting Jens H.J. Bos Rolf H. Sijmons Eelko Hak Bob Wilffert Submitted
c h a p te r S E V E N
Background
(Es)citalopram users commonly fail to achieve symptom remission in their first treatment episode. Drug-gene-interaction (DGI), drug-drug-interaction (DDI), and drug-drug-gene-interaction (DDGI) may influence the efficacy of (es)citalopram.
Aims
We aimed to explore the association between CYP2C19 and/or CYP3A4 mediated DGIs, DDIs, and DDGIs and (es)citalopram dispensing course among adults starting (es)citalopram.
Methods
An inception cohort study was conducted among adult Caucasians (≥18 years old) from the Lifelines cohort (167,729 participants) with available CYP2C19/3A4 genotypes and linked dispensing data from the University of Groningen prescription database IADB.nl as part of the PharmLines Initiative. Exposure groups were categorized into first-time users of (es)citalopram with (1) DGI (CYP2C19/3A4 deviating phenotype), (2) DDI (CYP2C19/3A4/2D6 inhibitors/inducers), and (3) DDGI (co-presence of DDI and DGI). Primary outcome was drug switching and/or dose adjustment, and the secondary outcome was early discontinuation within 90 days after the start of (es)citalopram. Logistic regression modeling was applied to estimate adjusted odd ratios with their corresponding 95% confidence interval.
Results
Overall, 316 (es)citalopram starters (median 45 years, 63% women) with complete CYP2C19/3A4 genetic information were identified. The DGIs between (es)citalopram and the combination of CYP2C19 intermediate metabolizer (IM)/poor metabolizer (PM) and CYP3A4 Normal Metabolizer (NM) increased the risks of switching and/or dose reduction (OR: 2.75; 95% CI: 1.03-7.29). The effect size increased for the combination of CYP2C19 IM/PM and CYP3A4 IM (OR: 4.38; 95% CI: 1.22-15.69). These associations on the primary outcome were likely dominated by the effect of CYP2C19 IM, which was primarily associated with an increased need of drug switching and/or dose reduction (OR:3.16; 95% CI: 1.41-7.09) and to a lesser extend early discontinuation (OR:0.35; 95% CI: 0.15-0.79). No participants with CYP2C19 UM and CYP3A4 NM/IM combination experienced drug switching and/or dose reduction and no significant association with early discontinuation as well as dose elevation were observed. Cumulatively, CYP2C19/3A4 mediated DDIs and DDGIs also showed a positive trend for drug switching and/or dose reduction ([OR:2.82; 95% CI: 0.49-15.97] and [OR:2.33; 95% CI: 0.42-12.78]).
Conclusions
A DGI involving predicted decreased function of CYP2C19 may increase the need for (es)citalopram switching and/or dose reduction. This effect might further be enhanced by the co-presence of predicted decreased function of CYP3A4. For DDI and DDGI, trends towards an increased occurrence of es(citalopram) switching and /or dose reduction were observed, but larger studies are needed to confirm these findings.
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Introduction
Selective serotonin re-uptake inhibitors (SSRIs) such as citalopram and escitalopram [(es)citalopram] are among the first-line pharmacological options recommended for depression in Europe and
the US, and the use of SSRIs has increased considerably over the years1,2. However, reports showed
that less than 50% of (es)citalopram users achieved disease symptom remission during their first
treatment episode, and prognosis appeared unpredictable3,4. Such variable effectiveness may be
explained by a large inter-individual pharmacokinetic variability among patients treated with (es)
citalopram5,6. This variability is known to be caused partly by differences in metabolic activity of drug
metabolizing Cytochrome P450 (CYP) enzymes7.
(Es)citalopram is primarily metabolized by the combination of CYP2C19 and CYP3A4 enzymes,
and to a lesser extent by CYP2D6 enzyme8,9. Genetic polymorphisms are known to affect the catalytic
activity of these enzymes. Some studies have investigated the role of CYP2C19 and CYP2D6
polymorphisms on the exposure as well as the clinical impact of (es)citalopram7,10,11. Such interaction
between the drug treatment and genetic variation is referred to as drug-gene interaction (DGI)12.
To the best of our knowledge, no previous studies have explored the impact of the DGI related to CYP3A4 polymorphisms, or its combination with CYP2C19 polymorphisms, in (es)citalopram treatment. In addition, the concomitant administration of CYP2C19, CYP3A4, and/or CYP2D6 (CYP2C19/3A4/2D6) modulator drugs (inhibitor/inducer) produces a drug-drug-interaction (DDI)
with (es)citalopram by affecting blood concentrations and hence modify its effectiveness13.
To make it even more complicated for treating physicians, (es)citalopram treatment may be affected by both genetics and drugs that modulate the activity of the metabolic pathways at the same time which potentially affect blood concentration even more unpredictable than DGI
and DDI alone14. In other words, a drug-drug-gene-interaction (DDGI) is encountered when a DGI
coincides with a DDI15,16. Generally, DDGIs show more pharmacokinetic diversity than DDIs and
DGIs alone, since DDGIs concern several modes of interactions 16. For example, a DDGI may involve
the existence of a genetic polymorphism and a CYP-inhibitor for one CYP-enzyme or the co-presence of a genetic polymorphism in one or two metabolic pathways and a CYP modulator in
another pathway15,16.
Due to restricted study populations in trials and scarcity of health care databases with a possibility to link genetic and drug dispensing data, large-scale real-world pharmacogenetic studies are lacking on the impact of pharmacogenetic and drug interactions in general. Consequently, recent guidelines have only provided specific recommendations on the management of (es)citalopram-related DGIs and DDIs separately, but a knowledge gap remains regarding the pharmacotherapeutic
management of DDGIs17,18. The PharmLines Initiative enables the unique linkage of genetic and drug
data to perform an inception cohort study in a large population cohort with the aim to explore the impact of DDIs, DGIs (spesifically CYP2C19 and CYP3A4 polymorphisms), and DDGIs on
short-term first-time (es)citalopram therapy19. To mirror treatment success, proxy outcomes such as
drug switching, dose adjustment, and an early discontinuation after the first prescription of (es)
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Method
Study design, setting and data sources
This retrospective inception cohort study was performed using data from the PharmLines Initiative which links data from the Lifelines cohort study and the University of Groningen prescription IADB.
nl database, two large databases in the Northern part of the Netherlands19.
The Lifelines cohort (https://www.lifelines.nl/researcher/about-lifelines) is a three-generation prospective cohort, with duration of thirty years, covering 167,729 Dutch participants from the Northern provinces of the Netherlands who were recruited within seven years from 2006 to
201322,23. It was established with the aim to study ‘complex interactions between environmental,
phenotypic and genomic factors in the development of chronic diseases and healthy ageing’22,23.
The participants from the Lifelines cohort were reported to generally represent the characteristics
of the adult population of the Northern part of the Netherlands24. More comprehensive information
about the Lifelines cohort can be found in the publications of Stolk et al. and Scholtens et al.22,23.
The University of Groningen prescription database IADB.nl collected over 1.2 million prescriptions from 72 pharmacies since 1994 to 2017. About 730,000 recorded anonymous patients have been assigned with a unique identifier and the information about their sex, date of birth and
four-digit postal codes (optional) were available25. The prescription information of each participants
was recorded such as dispensing date, the Anatomical Therapeutic Chemical code (ATC code),
quantity, duration, the DDD (defined daily dose), and the prescriber25. The participants recorded
in the IADB.nl are found to be representative of the general population in the Netherlands as
whole25. The IADB.nl has been widely used as a source of study population of many (pharmaco)
epidemiological studies26-28.
The linking process of these two databases was facilitated by, a trusted third party, the Statistic Netherlands. The linkage was performed at the individual level and relied on the combined information of identifying information including postal code, date of birth, and sex. Once the selection process was completed, identifiers from each database were cleared. Each participant was then given a new identifier unique for this study (pseudoID). Using the pseudoID, the genetic and prescription information of the participants from the Lifelines cohort and the IADB.nl, respectively, could be combined and analyzed. The details on the linking process of the two databases has been
published elsewhere19.
Study population
All adult Lifelines participants (Caucasian, 18 years and older) with available genetic information (CYP2C19 and CYP3A4 genes) and who had their first prescription of citalopram (N06AB04) or escitalopram (N06AB10) recorded in the PharmLines Iniative were eligible for this study. Those who were not prescribed any (es)citalopram for at least 180 days before starting their drug dispensing were included. If there were several periods of (es)citalopram dispensing, only the first dispensing period was included in the analysis. The date of the first prescription of (es)citalopram was regarded as the index date which indicates the start of follow-up.
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Genotyping
Genotyping for single-nucleotide polymorphism (SNP) of CYP2C19 and CYP3A4 genes in the Lifelines
cohort was performed using the Illumina CytoSNP-12v2 array22. The genotype data was imputed by
using the Genome of the Netherlands reference panel22. The quality of genotyping data was checked
using the following requirements i.e. (i) the P-value of Hardy-Weinberg equilibrium distribution
was >1e-4, (ii) call rate of 95%, and (iii) minor allele frequency (MAF) was > 0.00122. Additionally,
principal component analysis was used to detect statistical outliers22. More detailed information on
the genotyping process can be found in the publication of Scholtens et al (2014)22.
The genotypes of CYP2C19 and CYP3A4 were translated to haplotypes, which were used to predict corresponding phenotypes (details are displayed in supplementary material 1). Relevant haplotypes were selected and the genotypes were translated to the predicted phenotypes based on available information from the Royal Dutch Pharmacists Association-Dutch Pharmacogenetics Working Group (DPWG). CYP2C19 alleles included in this study were CYP2C19*1, CYP2C19*2,
CYP2C19*4, CYP2C19*5/*7, CYP2C19*8, and CYP2C19*17. CYP3A4 alleles included in this study were CYP3A4*1A, CYP3A4*1B, CYP3A4*1G, and CYP3A4*22. Corresponding predicted phenotypes include
poor metabolizer (PM), intermediate metabolizer (IM), and normal metabolizer (NM) for CYP2C19 and CYP3A4, and ultra-rapid metabolizer (UM) for CYP2C19.
Definition of exposures
The exposure groups were defined as (es)citalopram users with a DGI, DDI, or DDGI. A DGI was defined as the combination of (es)citalopram and CYP2C19 and/or CYP3A4 deviating predicted phenotypes. Participants who were predicted to be CYP2C19 UM, IM, or PM and/or CYP3A4 IM or PM and were prescribed (es)citalopram without co-prescription of CYP2C19/3A4/2D6 modulators (inhibitors/inducers) were classified as experiencing a DGI. For statistical power reasons, the IM and PM groups were pooled into a combined IM/PM group, but we also provided a sensitivity analysis for the separated IM and PM groups (supplementary material 2).
Participants were classified to have a DDI when they were predicted as normal metabolizers (NM) of CYP2C19 and CYP3A4, and at the same time were co-prescribed a CYP2C19 and/or CYP3A4 and/or CYP2D6 modulator during the (es)citalopram treatment within a follow-up time frame of 90 days. A list of clinically relevant CYP2C19/3A4/2D6 modulators was based on
Commentaren Medicatiebewaking (Health Base, NL) and the Flockhart tableTM, and can be found
in the supplementary 129,30. Only non-SSRI drugs were included as CYP2C19/3A4/2D6 modulators
since our study population consists of first-time (es)citalopram users and it is unlikely to combine
this with another SSRI drugs in the early phase of drug treatments31. The detection of another
SSRI during the first-time prescription of (es)citalopram is more likely due to a switching event
(see study outcomes)32.
Lastly, DDGI was defined as the occurrence of a DGI and DDI at the same time in which (es) citalopram patients with a CYP2C19/3A4 predicted deviating phenotype received a CYP2C19/3A4/2D6 modulator. The non-exposed reference group was defined as (es)citalopram users with a predicted
7
normal CYP2C19 and CYP3A4 (NM) and who were not prescribed any CYP2C19/3A4/2D6 modulator during first-time (es)citalopram treatment.
Study outcomes
Three study outcomes were used to explore the impact of the different exposures i.e. drug switching, dose adjustment and early discontinuation. The incidence of these outcomes within the time frame of 90 days follow-up after the index date were identified. This time frame was used since the acute phase treatment of SSRIs is considered to be between 6 and 12 weeks after the start of drug treatment. A previous report indicated that about 70% of antidepressant users stopped
their therapy within 90 days33. However, since interactions commonly have an immediate effect,
the presence of the outcomes within the time frame of 45 days follow-up after the index date
were also explored (supplementary materials 3)21. Drug switching was defined as patients having
an early discontinuation of (es)citalopram as well as the prescription of another antidepressant, regardless of the class, within 120 days after the index date. The follow-up time frame was expanded for dispensing of other antidepressants from 90 to 120 days after the index date in order to accommodate the possible time gap between the dispensing of (es)citalopram and the new
antidepressant32,34. Meanwhile, dose adjustment was defined as having a dose reduction or a dose
elevation for at least 25% of the first dose within 90 days after the index date. Early discontinuation was defined as discontinuing the prescription of (es)citalopram within 90 days after the index date, having no further re-prescription of (es)citalopram for at least 180 days after the stop date as well as no switching as described previously. In the preliminary analysis the effects of exposure on drug switching and dose reduction were in the same direction, therefore the outcomes were combined. Analysis on the separated outcomes are provided in the supplementary material 2.
Co-variates
The following co-variates were recorded to compare groups: age, sex, dose of (es)citalopram at the index date, number of prescriptions, and pre-defined drugs as a proxy for certain co-existing comorbidities. These proxy medications have been defined in previous studies to represent
the presence of certain comorbidities26,35,36. Diabetes was detected by using the prescriptions
of insulin and analogues (A10A) and/or blood glucose lowering drugs (A10B). Cancer was identified by the presence of antineoplastic agents (L01) prescriptions. Dementia was identified by using the prescription of anti-dementia drug (N06D). Asthma and COPD were identified by the prescriptions of drugs for obstructive airway diseases (R03). Dyslipidemia was identified by the prescriptions of lipid modifying agents (C10). Anxiety was detected by using the prescriptions of anxiolytics (N05AB) and/or hypnotics and sedatives (N05C). Hepatic related problems were identified by the presence of prescriptions for drugs used in alcohol dependence (N07BB), antivirals for treatment of HCV infections (J05AP), interferon (L03AB), and/or bile and liver therapy (A05). Cardiovascular related diseases were identified by the prescriptions of antithrombotic agents (B01A), organic nitrates (C01DA), digoxin (C01AA05), antihypertensive (C02), diuretics (C03), beta blocking agents (C07), ca-channel blockers (C08), and/or agents acting on the renin-angiotensin
7
system (C09). (Es)citalopram users had to have at least two prescriptions of these proxy medications within six months before or after the index date to be assumed as having a chronic condition of
the potential comorbidities35. The presence of NSAIDs co-prescription during (es)citalopram
prescription was checked within the time frame of 90 days since the combination of NSAIDs and
SSRIs was reported to increase the risk of gastro intestinal bleeding37. The potential comorbidities
were clustered into one group namely ‘potential comorbidities’ in order to increase the power of the calculation. The distribution for each potential comorbidities was compared separately between outcomes, none of them were statistically significant different (p< 0.05). Lastly, the distribution of the number of CYP2C19/3A4/2D6 modulator prescriptions during the use of (es)citalopram was compared, since a previous study indicated that the more number of CYP2C19/3A4/2D6 modulator,
the more alteration in the clearance of (es)citalopram13.
Statistical analysis
The Chi-square test (or Fisher’s exact test if more than 20% of cells had expected cell values below five) and Mann-Whitney test were used to compare the distribution of categorical and skewed distributed continuous variables between outcomes, respectively. The co-variates which differed significantly (P < 0.05) were entered into the final multivariate logistic regression model to obtain the adjusted odds ratio as measure of association (OR). Since some participants did not have dosing information, a complete case analysis in case of dosing comparison as well as dose adjustment analysis was performed. The baseline characteristics were compared between participants with complete information and participant without dosing information (supplementary material 2). The statistical analysis was performed using Statistical Program for Social Sciences (SPSS) version 24.0 for Windows.
Results
Overall, 316 (es)citalopram users (median 45 years, 63% women) with CYP2C19 and CYP3A4 genetic information were available (Figure 1). Baseline characteristics of patients are displayed in table 1. About 56% of the patients had at least one predicted deviating phenotype of CYP2C19 or CYP3A4 with CYP2C19 IM as the dominant phenotype (32.6%). About 18% of the participants was recorded with at least one of the CYP-modulator co-prescriptions and most of them were CYP2C19 inhibitors (14%). No combination of (es)citalopram with CYP2C19/3A4 inducer alone was identified. Two patients exposed to a combination of CYP modulators (one patient with a CYP2C19 and a CYP2D6 inhibitor, and one patient with a CYP2C19 inhibitor and a CYP3A4 inducer) were excluded since the number was too small to analyze. About 79% of (es)citalopram users were co-prescribed at least one drug for a chronic potential comorbidity with anxiety drugs as the most common drug (data not shown). Among our study population, we found that 9%, 47%, and 8.5% of participants were exposed to DDIs, DGIs, and DDGIs, respectively. Frequency of each type of DDGI is presented in table 2. Most of the DDGIs were caused by the combination of CYP2C19 deviating genotypes and CYP2C19 inhibitors (51.8%) affecting one metabolic pathway of (es)citalopram.
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Fi g ur e 1. S el ec ti o n o f ( es )c it al o pr am fi rs t ti m e us er s7
Table 1. Characteristics of patients starting (es)citalopram (n = 316)Variabels n
Sex (n women, %) 200 (63.3)
Age in years, median (IQR) 45 (14)
CYP2C19 Phenotypes CYP2C19 NM (n, %) 176 (55.7) CYP2C19 IM (n, %) 103 (32.6) CYP2C19 PM (n, %) 23 (7.3) CYP2C19 UM (n, %) 14 (4.4) CYP3A4 Phenotypes CYP3A4 NM (n, %) 254 (80.4) CYP3A4 IM (n, %) 56 (17.7) CYP3A4 PM (n, %) 6 (1.9)
Combination of CYP2C19 & CYP3A4 Phenotypes
CYP2C19 NM + CYP3A4 NM (n, %) 140 (44.3)
CYP2C19 IM/PM + CYP3A4 NM (n, %) 104 (32.9)
CYP2C19 IM/PM + CYP3A4 IM/PM (n, %) 20 (6.3)
CYP2C19 NM + CYP3A4 IM/PM (n, %) 36 (11.4)
CYP2C19 UM + CYP3A4 NM/IM (n, %) 14 (4.4)
Type of CYP modulator combination
No inhibitor or inducer of CYP2C19/3A4/2D6 260 (82.3)
CYP2C19 inhibitor alone (n, %) 44 (13.9)
CYP3A4 inhibitor alone (n, %) 4 (1.3)
CYP2D6 inhibitor alone (n, %) 6 (1.9)
CYP2C19 inhibitor + CYP2D6 inhibitor (n, %)* 1 (0.3)
CYP2C19 inhibitor + CYP3A4 inducer (n, %)* 1 (0.3)
DDD at start of citalopram and escitalopram
DDD < 1 (n, %) 25 (7.9) DDD >= 1 (n, %) 197 (62.3) No dose information (n, %) 94 (29.7) Potential comorbidities No comorbidity (n, %) 65 (20.6) 1-2 potential comorbidities (n, %) 216 (68.3) ≥3 potential comorbidities (n, %) 35 (11.1)
Number of co-prescriptions during (es)citalopram
1-3 type of drugs (n, %) 247 (78.2)
>3 type of drugs (n, %) 69 (21.8)
Number of CYP modulator during (es)citalopram
No CYP modulator (n, %) 260 (82.3)
1 CYP modulator (n, %) 27 (8.5)
≥2 CYP modulator (n, %) 29 (9.2)
Combined exposures No exposures
CYP2C19 NM + CYP3A4 NM + No CYP Modulator (n, %) 111 (35.1) DDI
7
Table 1. (Continued)
Variabels n
DGI
CYP2C19 IM/PM + CYP3A4 NM + No CYP Modulator (n, %) 89 (28.2) CYP2C19 IM/PM + CYP3A4 IM/PM + No CYP Modulator (n, %) 20 (6.3) CYP2C19 NM + CYP3A4 IM/PM + No CYP Modulator (n, %) 29 (9.2) CYP2C19 UM + CYP3A4 NM/IM + No CYP Modulator (n, %) 11 (3.5)
DDGI (n, %) 27 (8.5)
*Excluded
Table 2. Frequency of DDGI (overlapping condition of DDI and DGI)
CYP2C19 Phenotype CYP3A4 Phenotype CYP2C19 Inhibitor CYP3A4 Inhibitor CYP2D6 Inhibitor CYP2C19 Inducer CYP3A4 Inducer N (%) One pathway UM/IM/PM NM Y N N N N 14 (51.8%) Two pathways IM IM Y N N N N 2 (7.4%) IM NM N Y N N N 2 (7.4%) IM NM N N Y N N 2 (7.4%) NM IM/PM Y N N N N 6 (22.2%) NM IM N N Y N N 1 (3.7%) SUM 27
There were 25 (7.9%), 7 (2.2%), 80 (25%) and 47 (15%) (es)citalopram users experiencing drug switching, dose reduction, dose elevation and early discontinuation, respectively. (Es)citalopram was switched to other SSRIs (n=9; paroxetine, sertraline, fluoxetine), tricyclic antidepressants (n=7; amitriptyline, nortriptyline), SNRIs (n=6; venlafaxine, duloxetine), and atypical antidepressants (n=3; mirtazapine, agomelatine). A significant higher rate of switching was observed amongst (es) citalopram users with over 3 co-prescriptions (p-value = 0.02). Female gender and a higher dose at the index date are less prevalent in the subgroup that experienced dose elevation of (es)citalopram (p-value = 0.003 and 0.002, respectively) (table 3).
In our dataset, predicted decreased metabolic function of CYP2C19 mediated DGI, regardless of phenotypic status of CYP3A4, showed a higher risk of drug switching and/or dose reduction compared to CYP2C19 NM, but a lower risk of early discontinuation. These effects were mainly seen in CYP2C19 IM which showed significant associations with drug switching and/or dose reduction (aOR: 3.16, 95% CI: 1.41-7.09) and early discontinuation (aOR: 0.35, 95% CI: 0.15-0.79). CYP2C19 IM also showed a trend towards lowering the risk of dose elevation (aOR: 0.59, 95% CI: 0.31-1.12). Meanwhile, CYP2C19 PM showed no significant association with drug switching and/or dose reduction (aOR: 0.54, 95% CI: 0.07-4.52) but had a comparable trend with CYP2C19 IM on the risk of early discontinuation (aOR: 0.41, 95% CI: 0.09-1.89) and dose elevation (aOR: 0.56, 95% CI: 0.16-2.02) (table 4).
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Furthermore, there was an indication showing that the co-presence of CYP3A4 IM/PM in individuals with CYP2C19 IM/PM increased the risk of switching and/or dose reduction of (es) citalopram to a larger extent than the combination of CYP2C19 IM/PM and CYP3A4 NM (aOR: 4.38, 95% CI: 1.22-15.69 and aOR: 2.75, 95% CI: 1.03-7.29, respectively). This effect might be facilitated by the combination of CYP2C19 IM and CYP3A4 IM since there was only one participant with CYP2C19 PM and no participants with CYP3A4 PM experiencing switching or dose reduction (table 4). Meanwhile, CYP3A4 IM/PM in the co-presence of CYP2C19 NM did not seem to influence the risk of switching and/or dose reduction (aOR: 1.02, 95% CI: 0.19-5.24).
No participants with CYP2C19 UM and CYP3A4 NM/IM combination experienced drug switching and/or dose reduction and no significant association with early discontinuation was observed (aOR: 1.89, 95% CI: 0.45-8.07) as well as with dose elevation (aOR: 0.72, 95% CI: 0.12-4.35).
In our cohort, (es)citalopram users with DDIs seemed to increase the risk of drug switching and/ or dose reduction (aOR: 2.82, 95% CI: 0.49-15.97) which was mainly facilitated by the co-presence of CYP2C19 inhibitors (aOR: 2.36, 95% CI: 0.67-8.32). DDIs did not seem to increase the risk of dose elevation (aOR: 1.03, 95% CI: 0.34-3.12) and early discontinuation (aOR: 0.67, 95% CI: 0.20-2.21) (table 4).
Meanwhile, DDGIs also seemed to increase the risk of drug switching and/or dose reduction (aOR: 2.33, 95% CI: 0.42-12.78). There were only two participants with DDGIs experiencing drug switching or dose reduction, consisting of one participant with a DDGI affecting one pathway and the other one with a DDGI affecting two pathways. Consequently, a separated analysis of DDGIs based on the number of pathways affected produced comparable effect sizes i.e. one pathway (aOR: 2.52, 95% CI: 0.26-24.61) and two pathways (aOR: 2.17, 95% CI: 0.23-20.67) (supplementary material 2).
Analysis using a time frame of 45 days after the index date produced comparable results. CYP2C19 IM increased the risk of switching (aOR: 6.15, 95% CI: 1.64-23.09). The effect size of CYP2C19 IM was also larger in combination with CYP3A4 IM/PM (aOR: 6.41, 95% CI: 1.19-34.40) than with CYP3A4 NM (aOR: 2.66, 95% CI: 0.65-10.96). Additionally, CYP2C19 IM also showed to have an increased risk of dose reduction (aOR: 2.69, 95% CI: 0.43-16.96). Lastly, DDIs and DDGIs seemed to increase the risk of dose reduction and switching, respectively. Detailed data can be found in supplementary material 3.
Discussion
In this explorative inception cohort study, we presented for both CYP2C19 and CYP3A4 the associations of DGI, DDI and DDGI and the risk of switching or dose amendments and early discontinuation in the first treatment episode of (es)citalopram. In our relatively small samples, we found an indication that participants with DGI involving predicted CYP2C19 IM tended to experience switching or dose reduction, instead of early discontinuation, regardless of the CYP3A4 predicted phenotype. For participants with DGI involving predicted CYP3A4 IM/PM no influence on switching or dose reduction was found. Yet, the effect of CYP2C19 IM might be enhanced by the presence of CYP3A4 IM. DDI and DDGI might be associated with an increased risk of switching or dose reduction, but the associations were not significant with wide confidence intervals.
7
Ta b le 3 . B ase lin e c om p arison s V ar ia b le s Sw it ch in g* P-V al ue D ec re as ed D o se # P-V al ue In cr ea se d D o se # P-V al ue D is co n ti n ua ti o n* P-V al ue Ye s (n = 25 ) N o (n = 27 9) Ye s (n = 7) N o (n =2 13 ) Ye s (n = 80 ) N o (n = 14 0 ) Ye s (n = 4 7) N o (n = 25 7) Se x (n W o m en , %) 15 ( 60 ) 17 7 (6 3. 4) 0 .7 3 5 (7 1.4 ) 13 3 (6 2. 4) 1. 0 0 40 ( 50 ) 98 ( 70 ) 0 .0 0 3 29 ( 61 .7 ) 16 3 (6 3. 4) 0 .8 2 A ge in y ea rs ( m ed ia n, IQ R) 41 ( 14 ) 45 ( 13 ) 0 .6 8 39 ( 13 ) 42 ( 13 ) 0 .3 8 43 .5 ( 11 ) 41 ( 13 ) 0 .9 2 48 ( 19 ) 44 ( 12 ) 0 .0 3 D D D a t st ar t (n D D D > = 1, % ) 18 ( 72 ) 17 7 (6 3. 4) 1. 0 0 7 (1 0 0 ) 18 8 (8 8. 3) 1. 0 0 64 ( 80 ) 13 1 ( 93 .6 ) 0 .0 0 2 31 ( 66 ) 16 4 (6 3. 8) 0 .5 7 Po te nt ia l c o m o rb id it ie s (n Y es , % ) N o c o m o rbi d it y (n , % ) 3 (1 2) 60 ( 21 .5 ) 0 .18 3 (4 2. 9) 36 ( 16 .9 ) 0 .2 2 16 ( 20 ) 23 ( 16 .4 ) 0 .7 9 9 ( 19 .1) 54 ( 21 ) 0 .7 1 1-2 p o te nt ia l c o m o rb id it ie s (n , % ) 21 ( 84 ) 18 5 (6 6. 3) 4 (5 7.1 ) 15 2 (7 1.4 ) 55 ( 68 .8 ) 10 1 ( 72 .1) 34 ( 72 .3 ) 17 2 (6 6. 9) ≥ 3 p o te nt ia l c o m o rb id it ie s (n , % ) 1 ( 4) 34 ( 12 .2 ) 0 ( 0 ) 25 ( 11 .7 ) 9 (1 1. 3) 16 ( 11 .4 ) 4 (8 .5 ) 31 ( 12 .1) N o f c o -pr es cr ip ti o ns 1-3 (n , % ) 24 ( 96 ) 21 3 (7 6. 3) 0 .0 2 7 (1 0 0 ) 16 6 (7 7.9 ) 0 .3 5 63 ( 78 .8 ) 11 0 ( 78 .6 ) 0 .9 7 38 ( 80 .9 ) 19 9 (7 7.4 ) 0 .6 0 >3 ( n, % ) 1 ( 4) 66 ( 23 .7 ) 0 ( 0 ) 47 ( 22 .1) 17 ( 21 .3 ) 30 ( 21 .4 ) 9 (1 9. 1) 58 ( 22 .6 ) N o f C Y P m o d ul at o r pr es cr ipt io ns N o ( n, % ) 22 ( 88 ) 22 9 (8 2. 1) 0 .9 2 6 (8 5. 7) 17 7 (8 3. 1) 0 .7 3 69 ( 86 .3 ) 11 4 (8 1.4 ) 0 .6 2 41 ( 87 .2 ) 21 0 ( 81 .7 ) 0 .6 4 1 ( n, % ) 2 (8 ) 25 ( 9) 1 ( 14 .3 ) 18 ( 8. 5) 5 (6 .3 ) 14 ( 10 ) 4 (8 .5 ) 23 ( 8. 9) ≥2 ( n, % ) 1 ( 4) 25 ( 9) 0 ( 0 ) 18 ( 8. 5) 6 (7 .5 ) 12 ( 8. 6) 2 (4 .3 ) 24 ( 9. 3) *N o s ta rt /s to p d at e = 10 ; #no d o se in fo rm at io n = 947
Ta b le 4 . A ss o ci at io n b et w ee n e xp o su re s a nd o ut co m es 4 .1 . D rug s w it ch in g a nd /or do se r ed uc ti on V ar ia b le s Sw it ch in g a nd /o r d o se r ed uc ti o n U n iv ar ia te a n al ys is M ul ti va ri at e an al ys is Ye s (n = 31 , % ) N o (n = 27 3, % ) O R ( 95 % C I) P va lu e aO R ( 95 % C I) P va lu e C Y P2 C 19 & C Y P3 A 4 p re d ic te d p h en o ty p es C Y P2 C 19 p re d ic te d p he no ty p es * C Y P2 C 19 N M 12 ( 38 .7 ) 15 7 (5 7. 5) Re f. Re f. C Y P2 C 19 IM 18 ( 58 .1) 82 ( 30 ) 2. 87 ( 1. 32 -6 .2 5) 0 .0 1 3. 16 ( 1.4 1-7. 0 9) 0 .0 0 5 C Y P2 C 19 P M 1 ( 3. 2) 20 ( 7. 3) 0 .6 5 (0 .0 8-5. 30 ) 0 .6 9 0 .5 4 (0 .0 7-4. 52 ) 0 .5 7 C Y P2 C 19 U M 0 ( 0 ) 14 ( 5. 1) N A N A C Y P3 A 4 pr edi ct ed p he no ty p es ** C Y P3 A 4 N M 23 ( 74 .2 ) 22 0 ( 80 .6 ) Re f. C Y P3 A 4 IM 8( 25 .8 ) 47 ( 17 .2 ) 1. 63 ( 0 .6 9-3. 86 ) 0 .2 7 1. 37 ( 0 .5 5-3. 39 ) 0 .5 0 C Y P3 A 4 PM 0 ( 0 ) 6 (2 .2 ) N A N A C o m bi na ti o n o f p re d ic te d p he no ty p es ** * C Y P2 C 19 N M + C Y P3 A 4 N M 9 (2 9) 12 5 (4 5. 8) Re f. Re f. C Y P2 C 19 IM /P M + C Y P3 A 4 N M 14 (4 5. 2) 85 ( 31 .1) 2. 29 ( 0 .9 5-5. 52 ) 0 .0 7 2. 35 ( 0 .9 6-5. 76 ) 0 .0 6 C Y P2 C 19 IM /P M + C Y P3 A 4 IM /P M 5 (1 6. 1) 17 ( 6. 2) 4. 0 8 (1 .2 2-13 .6 3) 0 .0 2 3 .4 6 (1 .0 2-11 .7 5) 0 .0 5 C Y P2 C 19 N M + C Y P3 A 4 IM /P M 3 (9 .7 ) 32 ( 11 .7 ) 1. 30 ( 0 .3 3-5. 0 9) 0 .7 0 1.1 1 ( 0 .2 8-4. 43 ) 0 .8 8 C Y P2 C 19 U M + C Y P3 A 4 N M /I M 0 ( 0 ) 14 ( 5. 1) N A N A C Y P m o d ula to r # N o in hi bi to r/ in d uc er o f C Y P2 C 19 /3 A 4/ 2D 6 27 ( 87 .1) 22 4 (8 2. 1) Re f. Re f. C Y P2 C 19 in hi bi to r 4 (1 2. 9) 40 ( 14 .7 ) 0 .8 3 (0 .2 7-2. 49 ) 0 .7 4 2. 36 ( 0 .6 7-8. 32 ) 0 .18 C Y P3 A 4 in hi bi to r 0 ( 0 ) 4 (1 .5 ) N A N A C Y P2 D 6 in hi bi to r 0 ( 0 ) 5 (1 .8 ) N A N A C o m b in ed e xp o su re s^ N o e xp o su re s C Y P2 C 19 N M + C Y P3 A 4 N M + N o C Y P M o d ul at o r 7 (2 2. 6) 10 1 ( 37 ) Re f. Re f.7
4 .1 . ( con ti nue d ) Sw it ch in g a nd /o r d o se r ed uc ti o n U n iv ar ia te a n al ys is M ul ti va ri at e an al ys is V ar ia b le s Ye s (n = 31 , % ) N o (n = 27 3, % ) O R ( 95 % C I) P va lu e aO R ( 95 % C I) P va lu e D D I CY P2 C 19 N M + C Y P3 A 4 N M + Y es C Y P M o d ul at o r 2 (6 .5 ) 24 ( 8. 8) 1. 20 ( 0 .2 4-6. 16 ) 0 .8 3 2. 82 ( 0 .4 9-15 .9 7) 0 .2 4 D G I CY P2 C 19 IM /P M + C Y P3 A 4 N M + N o C Y P M o d ul at o r 13 (4 1.9 ) 71 ( 26 ) 2. 64 ( 1. 0 0 -6 .9 5) 0 .0 5 2. 75 ( 1. 0 3-7. 29 ) 0 .0 4 C Y P2 C 19 IM /P M + C Y P3 A 4 IM /P M + N o C Y P M o d ul at o r 5 (1 6. 1) 15 ( 5. 5) 4. 81 ( 1. 35 -1 7.1 2) 0 .0 2 4 .3 8 (1 .2 2-15 .6 9) 0 .0 2 C Y P2 C 19 N M + C Y P3 A 4 IM /P M + N o C Y P M o d ul at o r 2 (6 .5 ) 26 ( 9. 5) 1.1 1 ( 0 .2 2-5. 66 ) 0 .9 0 1. 0 2 (0 .19 -5 .2 4) 0 .9 8 C Y P2 C 19 U M + C Y P3 A 4 N M /I M + N o C Y P M o d ula to r 0 ( 0 ) 11 (4 ) N A N A D D G I 2 (6 .5 ) 25 ( 9. 2) 1.1 5 (0 .2 3-5. 89 ) 0 .8 6 2. 33 ( 0 .4 2-12 .7 8) 0 .3 3 A d ju st ed fo r: * C Y P3 A 4 ph en o ty p es , C Y P m o d ul at o r & N o f c o -p re sc ri pt io ns ; * *C Y P2 C 19 p he no ty p es , C Y P m o d ul at o r & N o f c o -p re sc ri pt io ns ; ** *C Y P m o d ul at o r & N o f c o -p re sc ri pt io ns ; #C Y P2 C 19 & CY P3A 4 ph en o ty p es & N o f c o -p re sc ri pt io ns ; ^ N o f c o -p re sc ri pt io ns .7
4 .2 . D o se e le va ti on V ar ia b le s D o se e le va ti o n U n iv ar ia te a n al ys is M ul ti va ri at e an al ys is Ye s (n = 80 , % ) N o (n = 14 0, % ) O R ( 95 % C I) P va lu e aO R ( 95 % C I) P va lu e C Y P2 C 19 & C Y P3 A 4 p re d ic te d p h en o ty p es C Y P2 C 19 p re d ic te d p he no ty p es * C Y P2 C 19 N M 51 ( 63 .7 ) 67 (4 7.9 ) Re f. Re f. C Y P2 C 19 IM 23 ( 28 .7 ) 56 (4 0 ) 0 .5 4 (0 .2 9-0 .9 9) 0 .0 5 0 .5 9 (0 .3 1-1.1 2) 0 .11 C Y P2 C 19 P M 4 (5 ) 10 ( 7.1 ) 0 .5 3 (0 .16 -1 .7 7) 0 .2 9 0 .5 6 (0 .16 -2 .0 2) 0 .3 8 C Y P2 C 19 U M 2 (2 .5 ) 7 (5 ) 0 .3 7 (0 .0 7-1. 88 ) 0 .2 3 0 .3 5 (0 .0 7-1. 85 ) 0 .2 2 C Y P3 A 4 pr ed ic te d p he no ty p es ** C Y P3 A 4 N M 61 ( 76 .3 ) 11 4 (8 1.4 ) Re f. Re f. Re f. C Y P3 A 4 IM 17 ( 21 .3 ) 24 ( 17 .1) 1. 32 ( 0 .6 6-2. 65 ) 0 .4 3 1.4 8 (0 .7 0 -3 .12 ) 0 .3 0 C Y P3 A 4 PM 2 (2 .5 ) 2 (1 .4 ) 1. 87 ( 0 .2 6-13 .5 9) 0 .5 4 1. 27 ( 0 .15 -1 0 .6 4) 0 .8 2 C o m bi na ti o n o f p re d ic te d p he no ty p es ** * C Y P2 C 19 N M + C Y P3 A 4 N M 40 ( 50 ) 56 (4 0 ) Re f. Re f. C Y P2 C 19 IM /P M + C Y P3 A 4 N M 21 ( 26 .3 ) 52 ( 37 .1) 0 .5 6 (0 .29 -1 .0 8) 0 .0 8 0 .6 9 (0 .3 5-1. 36 ) 0 .2 8 C Y P2 C 19 IM /P M + C Y P3 A 4 IM /P M 6 (7 .5 ) 14 ( 10 ) 0 .6 0 ( 0 .2 1-1. 69 ) 0 .3 4 0 .5 7 (0 .19 -1 .6 8) 0 .3 1 C Y P2 C 19 N M + C Y P3 A 4 IM /P M 11 ( 13 .8 ) 11 ( 7.9 ) 1.4 0 ( 0 .5 5-3. 54 ) 0 .4 8 1. 66 ( 0 .6 2-4. 49 ) 0 .3 1 C Y P2 C 19 U M + C Y P3 A 4 N M /I M 2 (2 .5 ) 7 (5 ) 0 .4 0 ( 0 .0 8-2. 0 3) 0 .2 7 0 .4 1 ( 0 .0 8-2. 18 ) 0 .2 9 C Y P m o d ula to r # N o in hi bi to r/ in d uc er o f C Y P2 C 19 /3 A 4/ 2D 6 69 ( 86 .3 ) 11 4 (8 1.4 ) Re f. Re f. C Y P2 C 19 in hi bi to r al o ne 9 (1 1. 3) 21 ( 15 ) 0 .7 1 ( 0 .3 1-1 .6 3) 0 .4 2 0 .8 0 ( 0 .3 3-1.9 5) 0 .6 3 C Y P3 A 4 in hi bi to r al o ne 2 (2 .5 ) 2 (1 .4 ) 1. 65 ( 0 .2 3-11 .9 9) 0 .6 2 2. 75 ( 0 .3 7-20 .7 4) 0 .3 3 C Y P2 D 6 in hi bi to r al o ne 0 ( 0 ) 3 (2 .1) N A N A C o m b in ed e xp o su re s ^ N o e xp o su re s C Y P2 C 19 N M + C Y P3 A 4 N M + N o C Y P M o d ul at o r 33 (4 1. 3) 46 ( 32 .9 ) Re f. Re f. D D I CY P2 C 19 N M + C Y P3 A 4 N M + Y es C Y P M o d ul at o r 7 (8 .8 ) 10 ( 7.1 ) 0 .9 8 (0 .3 4-2. 83 ) 0 .9 6 1. 0 3 (0 .3 4-3. 12 ) 0 .9 67
4 .2 . ( con ti nue d ) D o se e le va ti o n U n iv ar ia te a n al ys is M ul ti va ri at e an al ys is Ye s (n = 80 , % ) N o (n = 14 0, % ) O R ( 95 % C I) P va lu e aO R ( 95 % C I) P va lu e D G I CY P2 C 19 IM /P M + C Y P3 A 4 N M + N o C Y P M o d ul at o r 18 ( 22 .5 ) 42 ( 30 ) 0 .5 9 (0 .2 9-1. 22 ) 0 .16 0 .6 9 (0 .3 3-1.4 5) 0 .3 3 C Y P2 C 19 IM /P M + C Y P3 A 4 IM /P M + N o C Y P M o d ul at o r 6 (7 .5 ) 13 ( 9. 3) 0 .6 4 (0 .2 2-1. 87 ) 0 .4 2 0 .6 4 (0 .2 1-1.9 1) 0 .4 2 C Y P2 C 19 N M + C Y P3 A 4 IM /P M + N o C Y P M o d ul at o r 10 ( 12 .5 ) 9 (6 .4 ) 1. 55 ( 0 .5 7-4 .2 3) 0 .3 9 1. 60 ( 0 .5 6-4. 56 ) 0 .3 7 C Y P2 C 19 U M + C Y P3 A 4 N M /I M + N o C Y P M o d ula to r 2 (2 .5 ) 4 (2 .9 ) 0 .6 9 (0 .12 -4 .0 3) 0 .6 9 0 .7 2 (0 .12 -4 .3 5) 0 .7 2 D D G I 4 (5 ) 16 ( 11 .4 ) 0 .3 5 (0 .11 -1 .14 ) 0 .0 8 0 .4 8 (0 .14 -1 .6 1) 0 .2 3 A d ju st ed fo r: * C Y P3 A 4 ph en o ty p es , C Y P m o d ul at o r, s ex & d o se a t st ar t; * *C Y P2 C 19 p he no ty p es , C Y P m o d ul at o r, s ex & d o se a t st ar t; ** *C Y P m o d ul at o r, s ex & d o se a t st ar t; #C Y P2 C 19 & CY P3A 4 ph en o ty p es , s ex & d o se a t st ar t; ^ se x & d o se a t st ar t.7
4 .3 . E arl y d isc on ti nu at ion . V ar ia b le s Ea rl y d is co n ti n ua ti o n U n iv ar ia te a n al ys is M ul ti va ri at e an al ys is Ye s (n = 4 7, % ) N o (n = 25 7, % ) O R ( 95 % C I) P-va lu e aO R ( 95 % C I) P-va lu e C Y P2 C 19 & C Y P3 A 4 p re d ic te d p h en o ty p es C Y P2 C 19 p he no ty p es * C Y P2 C 19 N M 33 ( 70 .2 ) 13 6 (5 2. 9) Re f. Re f. C Y P2 C 19 IM 9 (1 9. 1) 91 ( 35 .4 ) 0 .4 1 ( 0 .19 -0 .8 9) 0 .0 3 0 .3 5 (0 .15 -0 .7 9) 0 .0 1 C Y P2 C 19 P M 2 (4 .3 ) 19 ( 7.4 ) 0 .4 3 (0 .0 9-1.9 6) 0 .2 8 0 .4 1 ( 0 .0 9-1. 89 ) 0 .2 5 C Y P2 C 19 U M 3 (6 .4 ) 11 (4 .3 ) 1.1 2 (0 .2 9-4. 26 ) 0 .8 6 1. 24 ( 0 .3 2-4. 88 ) 0 .7 5 C Y P3 A 4 ph en ot yp es ** C Y P3 A 4 N M 36 ( 76 .6 ) 20 7 (8 0 .5 ) Re f. Re f. C Y P3 A 4 IM 11 ( 23 .4 ) 44 ( 17 .1) 1.4 4 (0 .6 8-3. 0 4) 0 .3 4 1. 29 ( 0 .5 9-2. 84 ) 0 .5 1 C Y P3 A 4 PM 0 ( 0 ) 6 (2 .3 ) N A N A C o m bi na ti o n o f p re d ic te d p he no ty p es ** * C Y P2 C 19 N M + C Y P3 A 4 N M 24 ( 51 .1) 11 0 (4 2. 8) Re f. Re f. C Y P2 C 19 IM /P M + C Y P3 A 4 N M 10 ( 21 .3 ) 89 ( 34 .6 ) 0 .5 2 (0 .2 3-1.13 ) 0 .0 9 0 .4 5 (0 .2 0 -1 .0 2) 0 .0 6 C Y P2 C 19 IM /P M + C Y P3 A 4 IM /P M 1 ( 2. 1) 21 ( 8. 2) 0 .2 2 (0 .0 3-1. 70 ) 0 .15 0 .17 ( 0 .0 2-1. 39 ) 0 .10 C Y P2 C 19 N M + C Y P3 A 4 IM /P M 9 (1 9. 1) 26 ( 10 .1) 1. 59 ( 0 .6 6-3. 81 ) 0 .3 0 1.4 3 (0 .5 8-3. 53 ) 0 .4 4 C Y P2 C 19 U M + C Y P3 A 4 N M /I M 3 (6 .4 ) 11 (4 .3 ) 1. 25 ( 0 .3 2-4. 83 ) 0 .7 5 1.4 3 (0 .3 6-5. 69 ) 0 .6 1 C Y P m o d ula to r # N o in hi bi to r/ in d uc er o f C Y P2 C 19 /3 A 4/ 2D 6 41 ( 87 .2 ) 21 0 ( 81 .7 ) Re f. Re f. C Y P2 C 19 in hi bi to r al o ne 6 (1 2. 8) 38 ( 14 .8 ) 0 .8 1 ( 0 .3 2-2. 0 4) 0 .6 5 0 .6 8 (0 .2 6-1. 75 ) 0 .4 2 C Y P3 A 4 in hi bi to r al o ne 0 ( 0 ) 4 (1 .6 ) N A N A C Y P2 D 6 in hi bi to r al o ne 0 ( 0 ) 5 (1 .9 ) N A N A C o m b in ed e xp o su re s^ N o e xp o su re s C Y P2 C 19 N M + C Y P3 A 4 N M + N o C Y P M o d ul at o r 20 (4 2. 6) 88 ( 34 .2 ) Re f. Re f. D D I CY P2 C 19 N M + C Y P3 A 4 N M + Y es C Y P M o d ul at o r 4 (8 .5 ) 22 ( 8. 6) 0 .8 0 ( 0 .2 5-2. 58 ) 0 .7 1 0 .6 7 (0 .2 0 -2 .2 1) 0 .5 17
4 .3 . ( con ti nue d ) V ar ia b le s Ea rl y d is co n ti n ua ti o n U n iv ar ia te a n al ys is M ul ti va ri at e an al ys is Ye s (n = 4 7, % ) N o (n = 25 7, % ) O R ( 95 % C I) P-va lu e aO R ( 95 % C I) P-va lu e D G I CY P2 C 19 IM /P M + C Y P3 A 4 N M + N o C Y P M o d ul at o r 9 (1 9. 1) 75 ( 29 .2 ) 0 .5 3 (0 .2 3-1. 23 ) 0 .14 0 .4 4 (0 .19 -1 .0 6) 0 .0 7 C Y P2 C 19 IM /P M + C Y P3 A 4 IM /P M + N o C Y P M o d ul at o r 1 ( 2. 1) 19 ( 7.4 ) 0 .2 3 (0 .0 3-1. 83 ) 0 .17 0 .19 ( 0 .0 2-1. 53 ) 0 .12 C Y P2 C 19 N M + C Y P3 A 4 IM /P M + N o C Y P M o d ul at o r 8 (1 7) 20 ( 7. 8) 1. 76 ( 0 .6 8-4. 56 ) 0 .2 5 1. 52 ( 0 .5 7-4. 0 4) 0 .4 1 C Y P2 C 19 U M + C Y P3 A 4 N M /I M + N o C Y P M o d ula to r 3 (6 .4 ) 8 (3 .1) 1. 65 ( 0 .4 0 -6 .7 8) 0 .4 9 1. 89 ( 0 .4 5-8. 0 7) 0 .3 9 D D G I 2 (4 .3 ) 25 ( 9. 7) 0 .3 5 (0 .0 8-1. 61 ) 0 .18 0 .3 8 (0 .0 8-1. 75 ) 0 .2 1 A d ju st ed fo r: * C Y P3 A 4 ph en o ty p es , C Y P m o d ul at o r & a ge ; * *C Y P2C 19 p he no ty p es , C Y P m o d ul at o r & a ge ; * ** C Y P m o d ul at o r & a ge ; #C Y P2 C 19 & CY P3A 4 ph en o ty p es & a ge ; ^ ag e7
About one in five adult (es)citalopram users in the Pharmlines Initiative cohort were not taking (es)citalopram continuously and either stopped or switched in the first episode of their treatment. Besides of adverse drug reactions (ADR) or lack of efficacy, there are other reasons for patients to stop taking their antidepressant drug such as assuming that the medication is not working, feeling healthy, avoiding drug dependence, not trusting the drug, seeking alternative solution without
drugs, uncomfortable feeling, stigma and being afraid of DDI and side-effects38-42. Therefore,
early discontinuation cannot always be directly related to drug effects. It has been reported that
only nine percent of patients stopped their medication because of prescribing doctors’ advice40.
Additionally, there has been reports that about 24% to 63% of discontinuers did not inform their GP
after stopping their medication39,40. Switching may indicate patients’ adherence to the depression
treatment and may justify that this treatment modifications were possibly related to the effects
of the drug43,44. Switching to another antidepressant has been used by other reports to indicate
the treatment failure of (es)citalopram20,21. In addition, since most of SSRIs induced side effects
are dose dependent, another strategy reported to diminish the side effects is dose reduction44,46.
Therefore, we assumed that switching or dose reduction are more reliable outcomes associated with the effects of (es)citalopram than early discontinuation.
We found that participants with CYP2C19 IM were more likely to experience switching than those with NM. This is consistent with the study reported by Mrazek et al. which showed that individuals with CYP2C19 reduced catalytic function were less tolerant to citalopram than those with increased
catalytic function47. It is probably because of the increase of (es)citalopram blood concentration
due to the low metabolic capacity of CYP2C197,48. We also found that (es)citalopram users with
CYP2C19 IM tended to experience dose reductions more than those with CYP2C19 NM. Decreasing the maximum daily dose of (es)citalopram in patients with CYP2C19 IM by 25% of the normal
maximum dose is recommended by the DPWG49. However, the US based Clinical Pharmacogenetics
Implementation Consortium (CPIC), has not recommended any dose adjustment for CYP2C19 IM17.
The possible underlying reasons for the discrepancy are the differences in the methodology of
assessing available evidence as well as producing therapeutic recommendations50. As a note, we
possibly managed to find some associations on CYP2C19 IM and the outcomes because we had a large enough number of (es)citalopram users with the genotype (about 33% of the cohort).
Unfortunately, we did not find any significant association between patients with CYP2C19 PM and UM to the outcomes which was probably due to a limited sample size. Some clinical studies reported that patients with CYP2C19 PM were exposed to (es)citalopram blood concentration to a greater extent than CYP2C19 IM and that patients with CYP2C19 UM had a lower exposure to (es)
citalopram compared to CY2C19 NMs7. Jukic et al. using about 2,000 genotyped persons from
the Oslo population found that escitalopram users with CYP2C19 UM and PM (33% of the study population) had a three times higher odds of switching to another antidepressants than those with
CYP2C19 NM20.
To the best of our knowledge, this is the first study to examine the impact of CYP3A4 alone and in combination with CYP2C19 on (es)citalopram treatment. Decreased function of CYP3A4 in the CYP2C19 NM participants did not seem to influence the outcomes, but might have increased the effect of CYP2C19 IM. A comparable trend of effects has been reported for CYP2D6. The effect
7
of CYP2D6 variant in individuals with CYP2C19 NM on the AUC of citalopram was limited. However, when there was a co-presence of CYP2C19 *1/*2 (IM), the influence of CYP2D6 *1/*4 (IM) became
stronger10. CYP2C19, CYP3A4 and CYP2D6 have different metabolic contribution in the (es)citalopram
biodegradation with CYP2C19 as the most important contributor8,9. Consequently, the decreased
metabolic function of CYP3A4 or CYP2D6 might not substantially alter (es)citalopram metabolism if the catalytic function of CYP2C19 is intact. The reduced metabolic function of those enzymes might be compensated by the metabolic activity of CYP2C19. However, the effects of genetic variation of CYP3A4 or CYP2D6 may be more apparent if there is an alteration on CYP2C19 activity.
In our dataset, there were about nine percent of (es)citalopram users exposed to potential DDIs. It might be because about 79% of our study population had at least one comorbidity and therefore, they used other drug(s) which might potentially interact with (es)citalopram. In this study, the potential DDIs were mainly represented by CYP2C19 inhibitors especially proton pumps inhibitors (PPIs) (data not shown). The magnitude of the inhibition depends on the type of PPIs with omeprazole producing the highest inhibition on (es)citalopram metabolism followed by
esomeprazole and then, lansoprazole52. Omeprazole was reported to increase s-citalopram plasma
concentration by about 50% to 120%53,54. Therefore, it has been recommended that patients with
omeprazole or esomeprazole should have a dose adjustment of (es)citalopram52. Cimetidine, another
CYP2C19 inhibitor, was also reported to increase the AUC values of citalopram and escitalopram
by approximately 41% and 72%, respectively53,55. FDA has issued a warning against this DDI, and
recommends 20 mg as the maximum dose of citalopram with cimetidine as co-medication56.
Although we do not find any significant associations between DDGI and the outcomes, this study is the first to explore the impact of the complex exposure on the (es)citalopram treatment at
the population level. Generally, DDGI may come in two main scenarios15,16. Firstly, it may only affect
one metabolic pathway of a drug for example overlapping condition between a CYP2C19 UM/IM/ PM and a CYP2C19 inhibitor in (es)citalopram users. In this scenario, we might expect that the level of blood concentration of (es)citalopram in an individual with a CYP2C19 UM and a CYP2C19 inhibitor
might be different from an individual with a CYP2C19 IM and a CYP2C19 inhibitor16. It is because
the higher the number of active allelic variants in the CYP450, the more difficult their phenotypes
can be converted by the co-presence of inhibitors57. The second main scenario is the alteration of
two or even three metabolic pathways of a drug. The alteration can be a result of the presence of deviating genotypes in one or two metabolic pathway(s) and the presence of CYP modulator in one or two other pathway(s). In this scenario, each possible combination of co-inhibition produced by genetic variation and CYP modulator might result in variation of (es)citalopram concentration
in the blood16. Therefore, the effect of DDGI can vary depending on the scenario of interaction,
the metabolic contribution of the inhibited pathway(s), and the potency of CYP modulator16. In this
study, since we had only two patients with DDGI experiencing switching, we could not explore more about the impact of the different scenarios on the outcomes. Since the number of patients with DDI and DGI were limited, we could expect that the number of patients exposed to DDGI was even less. Hence, further study with a larger dataset is warranted.
Since genotyping is still not a part of routine clinical testing, prescribers often have no indication about the genotype of the patients at the time of prescription. Consequently, the presence of DGI
7
and DDGI related to (es)citalopram, exposing 56% of our study population, is potentially missed by health practitioners. Therefore, in order to avoid DGI and DDGI complex interaction, pre-emptive genotyping, inclusion of genetic information in electronic health records as well as a sophisticated computerized drug interaction surveillance system are needed in clinical practice. Bradley et al. (2017) reported that pharmacotherapy based pharmacogenetics substantially enhanced the treatment outcomes of mental health disorders, such as depression and anxiety, in various
healthcare facilities58. Additionally, pharmacogenetics testing based therapies have been reported
as a cost-effective strategy to be implemented in health care59.
Several potential limitations need to be discussed. First, we had a relatively small sample size to accurately detect the effects of DGI involving CYP2C19 PM and UM as well as the impact of DDGI. Further studies are needed to provide solid evidence about the impact of DDGI in clinical practice which can be used to support the lack of pharmacotherapeutic management of DDGI in the current guidelines. Next, we did not have data on the blood concentration of (es)citalopram as the best indicator to show the effect of interactions. Thus, we cannot validate the associations between the exposures and the outcomes. In addition, we did not have information about the genotype status of CYP2D6, therefore, we could not assess the combined effects of CYP2C19/3A4/2D6 polymorphisms on (es) citalopram efficacy. The co-presence of CYP2D6 deviating genotypes may augment the combined effects of CYP2C19 and CYP3A4 polymorphisms. Subsequently, we did not have information about the prescribed indication of (es)citalopram. In the Netherlands, these drugs are mainly indicated to
treat depression and for off-label used, to treat anxiety disorders60. Moreover, we also did not have
information about the type of prescribers. Psychiatrists treated patients are probably in different
conditions than those who are treated by GPs61. Additionally, it was reported that psychiatrists are
mostly aware and positive about the usefulness of pharmacogenetics in supporting the effectiveness
of antidepressants62. These factors might influence our results. However, it was also reported
that most of the depressed patients are treated in primary care63. Furthermore, since we used
a prescription database, there is always the chance of patients not taking the prescribed medications and we could not ascertain the real condition of drug use behavior of patients. Consequently, we may underestimate the number of patients with early discontinuation. Next, we only used proxy medications to estimate the presence of some comorbidities which might not represent the real condition of patients. Using the proxies, we did not find significant differences in the distribution of potential comorbidities between outcomes. Beside switching or dose adjustment, another strategy to manage SSRIs induced side effects, including physical and psychological related side effects, is to
add another drug for treating side effects43. However, because there is a wide range of possible side
effects and drugs that can be used to relieve them, we could not include the addition of these drugs as outcome. Nevertheless, most clinicians were reported to favor switching above prescribing an
adjuvant drug for treating side effects43. Additionally, patients with UM have a tendency towards
treatment failure because of the lower concentration of (es)citalopram7. However, we did not find
any indication of the dose elevation in the patients with CYP2C19 UM. Another strategy to improve
inadequate treatment is addition of another antidepressant64. We did not include this treatment
strategy in this study as outcomes, therefore, we did not have any data about antidepressant combination. Nonetheless, it was reported that some patients and prescribers chose switching
7
over drug combination because of several reasons such as minimizing cost, reducing possibility of
drug interaction, increasing adherence and decreasing patient’s burden65. Lastly, about 30% of our
dataset had no information about the dose of (es)citalopram. The missingness may probably not be related to other variables since it may be because pharmacists or pharmacy technicians forgot putting the dose information before sending the prescription data to the IADB.nl. In the baseline comparisons, we found that patients without dosing information were significantly older than those with complete information (supplementary material 2). Hence, we might underestimate the effect of age on the dose amendments of (es)citalopram. However, among those with complete information, age seemed not to influence the dose elevation or reduction of (es)citalopram (table 3).
In conclusion, the predicted CYP2C19 IM phenotype increased the need of drug switching and/ or dose reduction, and the co-presence of CYP3A4 IM might enhance its effects. For DDI and DDGI, we found some indications about the direction of their effects. However, because of the wide confidence intervals, which were caused by small sample size, there were great uncertainties surrounding the estimates and the results should be interpreted cautiously. Therefore, further real-world studies with a larger sample are needed to confirm the results.
Acknowledgement
We thank Centraal Bureau voor de Statistiek (CBS) for the efforts to link the Lifelines and the IADB. nl. We also thank all participants in the IADB.nl and Lifelines cohort for providing the data used in this study.
Funding
Lifelines is financially supported by several parties such as the Dutch Government, The Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the European fund for regional development, Dutch Ministry of Economic Affairs, the Northern Netherlands Collaboration of Provinces (SNN), Provinces of Groningen and Drenthe, Pieken in de Delta, University Medical Center Groningen, and University of Groningen the Netherlands. Meanwhile, The prescription database IADB.nl and the PharmLines Initiative are financially supported by the Groningen Research Institute of Pharmacy, University of Groningen. Muh. Akbar Bahar obtained a DIKTI scholarship from the Ministry of Research, Technology and Higher Education of Indonesia. The funding organizations had no role and influence in the study design and results.
Competing interests
No conflicts are declared.7
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